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EUSN 2022 Workshop - blockmodeling

Basic info

Time: Monday, 12th September, 13:30-16:30.

Location: Devonport 005, Devenport House Building, Greenwich Campus, University of Greenwich, London.





  • Working R environment (I recommend R Studio, but essentially anything you are familiar with) with fairly recent R (4.0.2 and later are ok, but the exact version is not important, more recent is better)
  • The package blockmodeling:
    • Preferably version from R-forge, which can be installed using install.packages("blockmodeling", repos=""). Warning: R-forge always contains the latest versions, but they are sometimes not sufficiently tested.
    • Almost as good version 1.1.3 CRAN, which can be installed using install.packages("blockmodeling")

Very useful

  • The packages that blockmodeling package suggest - sna, doRNG, doParallel, foreach.
    Can be installed using: install.packages(c("sna", "doRNG", "doParallel", "foreach"))
  • The content of the Data and code Zip file extracted to your computer.


The workshop will cover generalized blockmodeling (Doreian et al., 2005; Žiberna, 2007) of mainly one-mode binary and valued networks in R using “blockmodeling” package blockmodeling R package. Only basic knowledge of R and networks/graphs is required.

The workshop will cover matrix representation of the network, plotting of such matrices, and of course, clustering the units in the network, that is blockmodeling. Clustering units based on structural, regular and generalized equivalence will be covered. The later implies that also prespecified blockmodeling will be covered.

All aspects of blockmodeling with the blockmodeling package from preparing the data through calling the optimization function (including setting appropriate parameters) to plotting and interpreting the results will be covered. In case of sufficient time and expressed interest, blockmodeling two-mode, multilevel, and linked networks can also discussed.

Some background literature:

  • Doreian, P., Batagelj, V., Ferligoj, A., 2005. Generalized blockmodeling. Cambridge University Press, New York.
  • Matjašič, M., Cugmas, M., & Žiberna, A., 2020. blockmodeling: An R package for generalized blockmodeling. Advances in Methodology and Statistics, 17(2), 49–66.
  • Žiberna, A., 2007. Generalized blockmodeling of valued networks. Social Networks 29, 105–126.
workshops/eusn22/start.txt · Last modified: 2022/09/10 22:24 by aleszib